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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?

A) Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups.
B) Use Pandas DataFrames to manage the graph structure and leverage Pandas indexing for efficient queries.
C) Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph.
D) Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently.

2. A data scientist needs to process a dataset containing 10 million records, performing transformations and exploratory data analysis (EDA). The processing needs to be efficient but does not require high- performance multi-GPU execution.
Which of the following libraries provides the best balance between usability and performance?

A) cuDF, since GPU acceleration will still provide a speedup even for moderately sized datasets.
B) Dask DataFrame, since it automatically parallelizes computations even when the dataset fits in memory.
C) Pandas, as it provides a simple API and works well for datasets that fit within system memory.
D) Spark DataFrame, as it is optimized for distributed processing and scales well even for 10 million records.

3. What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?

A) It provides GPU optimization to handle high-throughput inference workloads.
B) It only supports TensorFlow models for inference.
C) It is designed solely for edge devices and not for data centers.
D) It automatically tunes hyperparameters for all models.

4. A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?

A) Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
B) Implement ETL processes using only SQL-based transformations within a relational database system.
C) Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.
D) Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.

5. A machine learning engineer is working on an image classification problem where the dataset is small and lacks variability. To improve generalization, the engineer decides to augment the dataset using NVIDIA RAPIDS.
What is the best method to generate synthetic data efficiently while leveraging GPU acceleration?

A) Use traditional CPU-based augmentation techniques like OpenCV to transform images and generate new data.
B) Use cuML.PCA() to reduce dimensionality and create synthetic samples by reconstructing the data with added noise.
C) Apply cuML.GaussianMixture() to generate new synthetic data points based on an estimated probability distribution.
D) Use cuDF with cudf.DataFrame.sample() to create new samples by randomly selecting existing rows.

Solutions: